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In this work, we consider the performance of using a quantum algorithm topredict a result for a binary classification problem if a machine learningmodel is an ensemble from any simple classifiers. Such an approach is fasterthan classical prediction and uses quantum and classical computing, but it isbased on a probabilistic algorithm. Let $N$ be a number of classifiers from anensemble model and $O(T)$ be the running time of prediction on one classifier.In classical case, an ensemble model gets answers from each classifier and"averages" the result. The running time in classical case is $O\left( N \cdot T\right)$. We propose an algorithm which works in $O\left(\sqrt{N} \cdotT\right)$.

The Quantum Version of Prediction for Binary Classification Problem by Ensemble Methods